MNIST Example
=============

This example demonstrate how to convert keras code into CANDLE
compliant. Please refer
`code <https://github.com/ECP-CANDLE/Candle/tree/master/examples/mnist>`__
for more detail.

Here is the list of files,

-  mnist.py: CANDLE class
-  mnist_cnn.py and mnist_mlp.py: original mnist implementation from
   keras project
-  mnist_cnn_candle.py: mnist_cnn.py converted in CANDLE compliant mode
-  mnist_mlp_candle.py: mnist_mlp.py converted in CANDLE compliant mode
-  mnist_params.txt: model parameters are stored in a file for
   reproduciblity

::

   $ python mnist_cnn_candle.py -e 3
   Using TensorFlow backend.

   Importing candle utils for keras
   Params:
   {'activation': 'relu',
   'batch_size': 128,
   'data_type': <class 'numpy.float32'>,
   'epochs': 3,
   'experiment_id': 'EXP000',
   'gpus': [],
   'logfile': None,
   'optimizer': 'rmsprop',
   'output_dir': '/Users/hsyoo/projects/CANDLE/Benchmarks/examples/mnist/Output/EXP000/RUN000',
   'profiling': False,
   'rng_seed': 7102,
   'run_id': 'RUN000',
   'shuffle': False,
   'timeout': -1,
   'train_bool': True,
   'verbose': None}
   Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz
   11493376/11490434 [==============================] - 2s 0us/step
   x_train shape: (60000, 28, 28, 1)
   60000 train samples
   10000 test samples
   Instructions for updating:
   Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.

   Instructions for updating:
   Use tf.where in 2.0, which has the same broadcast rule as np.where
   Train on 60000 samples, validate on 10000 samples
   Epoch 1/3
   2020-05-13 11:53:17.373979: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA
   To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags.
   2020-05-13 11:53:17.374474: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 16. Tune using inter_op_parallelism_threads for best performance.
   60000/60000 [==============================] - 56s 932us/step - loss: 0.2719 - acc: 0.9157 - val_loss: 0.0683 - val_acc: 0.9774
   Epoch 2/3
   60000/60000 [==============================] - 55s 909us/step - loss: 0.0904 - acc: 0.9733 - val_loss: 0.0411 - val_acc: 0.9872
   Epoch 3/3
   60000/60000 [==============================] - 55s 909us/step - loss: 0.0666 - acc: 0.9808 - val_loss: 0.0339 - val_acc: 0.9893
   Test loss: 0.03386178284487105
   Test accuracy: 0.9893